Econometric Model of Carbon Dioxide Consumtion

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Group 3

Alok Kumar

N.R. Sudheendra

Pravin Agrawal

S.S. Hooda

Santha mani

Econometric Model for Carbon Dioxide Emission

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IntroductionSome DefinitionMethodologyLiterature StudyCollection of DataDeciding Economic Theory and its

assumptionsDevelopment of Model with E-viewChecking of Auto correlationChecking of MulticolliniartityDevelopment of Final ModelAnalysis of resultConclusion

Structure of Presentation

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“While we already have one of the lowest emissions intensity of the economy, we will do more. We are targeting a further

emissions intensity decline of 20-25% by 2020 on 2005 levels”. J Ramesh Copenhagen (16 Dec 2009)

What is emission intensityWhat is the trend of emission intensity and how

it affects Co2 emissionWhat are other factors that affects Co2 emissionCan we afford to promise reduction in Co2

emission by 2020 This presentation is an attempt to answer some of

these questions using Econometric model

Introduction

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Carbon Credit: Carbon Credits are part of a tradable permit scheme. They provide a way to reduce greenhouse gas emissions by giving

them a monetary value. A credit gives the owner the right to emit one tonne of carbon dioxide . One carbon credit is equal to one ton of carbon dioxide, or in some

markets, carbon dioxide equivalent gases. Energy Intensity:

Energy intensity is a measure of the energy efficiency of a nation's economy. It is calculated as units of energy per unit of GDP.

High energy intensities indicate a high price or cost of converting energy into GDP.

Low energy intensity indicates a lower price or cost of converting energy into GDP.

Carbon intensity : The carbon intensity, also called per capita annual emissions, is a

measure of how much carbon equivalents (CO2e) are emitted per capita of GDP.

The inverse is the metric called carbon productivity which is the amount of GDP product per unit of carbon equivalent

Some definition

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After literature review possible economic theory is defined

Based on the theory, series of Independent variables were identified responsible for Co2 Emission of the country, which is considered as dependent variable

Data collected for various identified Independent variables

Data entered in E-view Software to identify relationships between dependent and independent variables

Application of various checks like Auto correlation and Multicolliniarity

Final Regression equation i.e Model obtainedAnalysis of Model

Methodology

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Speech of Jayaram Ramesh at Copenhagen: 16th Dec 2009“We are targeting a further emissions intensity

decline of 20-25% by 2020 on 2005 levels” This statement set the tone of this presentation

India's GHG Emissions Profile - Results of Climate Modelling Studies: MOEF PublicationHelp us to know the trend of Co2 emission

Indicators of carbon emission intensity from commercial energy use in India: Barnali NagU, Jyoti ParikhHelp us to know some of the factors that may be

considered as independent variables

Literature Study

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Examining carbon emissions economic growth nexus for India: A multivariate cointegration approach: Sajal GhoshHelp us to narrow down our variables to few variables

India one of the least Carbon Intensive Countries in the World: McKinsey Reports: http://ecolocalizer.com/2009/05/24/india-one-of-least-carbon-intensive-countries-in-the-world-mckinsey-reports/#comments Help us to know in details about Energy Intensity and India’s

approachWorld Bank Says India Right In Resisting Mandatory Emission

Reductions: http://redgreenandblue.org/2009/05/09/world-bank-says-india-right-in-resisting-mandatory-emission-reductionsHelp us to analyze the findings with respect to India Climate

Change Stand

Literature Study

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After literature Review variables identified are

Dependent Variable: Co2 Emission of the country

Independent Variables:PopulationReal GDPCarbon IntensityEnergy IntensityIndustrial Development IndexAgricultural Production Index

And so on

Identification of Variables

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For collection of data took maximum effortAll data was not available at one placeRBI website main source of data for following variables:

Real GDP Industrial Development IndexAgricultural Production Index

Website of US Energy Information Administration; main source for Carbon IntensityEnergy IntensityPopulation and Co2 Emission of the country

Data for all above variables available for 1980 to 2007 As such data obtained for this period only.

Collection of Data

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All above data then studied individually with regard to its trend and interrelationship.

It is observed that Industrial production index and agriculture production Index could not give any idea about Co2 emission. Hence both these variables dropped from study.

It was assumed that there is linear relationship between dependent and Independent Variables.

Only basic Econometrics tools applied in preparation of Model

Economic Theory and its assumptions

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Co2/GHG is being emitted in environment by various means. In the present study Co2 /GHG emission considered is only for energy consumption.

Co2 means Co2 equivalent of all GHGBasic economic theory for which Hypothesis

testing done is that Co2 Emission is dependent on Population, Carbon Intensity, Real GDP and Energy Intensity.

Proposed theoretical relation is

Economic Theory and its assumptions

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All the data collected is converted to Proper Unit (easy to Understand)

Final Excel sheet preparedThis excel sheet is exported to Eview and first

trial attemptedThis will be shown on Eview Software

Development of Model with E-view

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Trail 1: ResultDependent Variable: CO2EMI_MMTMethod: Least SquaresDate: 04/13/10 Time: 19:05Sample (adjusted): 1 28Included observations: 28 after adjustments

VariableCoefficie

nt Std. Error t-Statistic Prob.

C-

928.4931 89.30247 -10.39717 0.0000CI_MT_CO2_PER_M_I

NR 40.46478 34.52117 1.172173 0.2531EI_TOE_PER_M_INR 16.63005 121.3475 0.137045 0.8922

GDP_RS_CRORE 0.000341 3.37E-05 10.12545 0.0000POPMIL 0.676503 0.158992 4.254950 0.0003

R-squared 0.997314 Mean dependent var 755.3959Adjusted R-squared 0.996847 S.D. dependent var 310.8044S.E. of regression 17.45096 Akaike info criterion 8.717099Sum squared resid 7004.327 Schwarz criterion 8.954992

Log likelihood-

117.0394 F-statistic 2135.362Durbin-Watson stat 0.631479 Prob(F-statistic) 0.000000

Check for AutocorrelationSince this is a time series data, we can see from Durbin Watson statistics, i.e. 0.63 that shows there is positive auto correlation

Check of Multicolliniarity test;

A statistically significant F-value (0.00) while some of the t-values are statistically insignificant for EI and CI.

Thus Model is not statistically significant

Requires corrections

Let us first address problem of Autocorrelation, by introducing error term

Let us see on E-view

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Trail 2: Result Check for Autocorrelation

Now, problem of autocorrelation has been taken care of.


A statistically significant F-value (0.00) while statistically insignificant t-values for EI, shows presence of Multicoliniarity

Let us first address problem of Multicolliniarity step by step

Dependent Variable: CO2EMI_MMT

Method: Least Squares

Date: 04/13/10 Time: 19:09

Sample (adjusted): 2 28

Included observations: 27 after adjustments

Convergence achieved after 10 iterations

Variable Coefficient Std. Error t-Statistic Prob.

C -1063.088 117.2552 -9.066449 0.0000

EI_TOE_PER_M_INR -58.28308 67.91927 -0.858123 0.4005

GDP_RS_CRORE 0.000348 2.65E-05 13.14918 0.0000

POPMIL 0.732708 0.153092 4.786049 0.0001

CI_MT_CO2_PER_M_INR 71.10024 19.42040 3.661111 0.0015

AR(1) 0.608627 0.117353 5.186272 0.0000

R-squared 0.999008 Mean dependent var 772.5153

Adjusted R-squared 0.998771 S.D. dependent var 302.9736

S.E. of regression 10.61989 Akaike info criterion 7.756464

Sum squared resid 2368.423 Schwarz criterion 8.044428

Log likelihood -98.71227 F-statistic 4228.066

Durbin-Watson stat 1.907018 Prob(F-statistic) 0.000000

Inverted AR Roots .61

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Test for Multicolliniarity

CI_MT_CO2_PER_M_INR EI_TOE_PER_M_INR POPMILGDP_RS_CRORECI_MT_CO2_PER_M_INR 1.000000 0.995595 -0.301992 -0.499305EI_TOE_PER_M_INR 0.995595 1.000000 -0.300249 -0.500036POPMIL -0.301992 -0.300249 1.000000 0.964908GDP_RS_CRORE -0.499305 -0.500036 0.964908 1.000000

Check for Multicolliniarity :

1.Test for group Correlation: (let us see on E view)

This shows strong relationship between EI and CI and also between GDP and Population

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Dependent Variable: CI_MT_CO2_PER_M_INR

Method: Least SquaresDate: 04/15/10 Time: 12:58Sample (adjusted): 1 28Included observations: 28 after adjustments

Variable Coefficient Std. Errort-Statistic Prob.

C -1.8531830.368426 -5.0299960.0000GDP_RS_CRORE 9.31E-08 1.98E-07 0.469413 0.6430POPMIL -0.0004590.000935 -0.4908000.6280EI_TOE_PER_M_INR 3.460497 0.126051 27.45317 0.0000

R-squared 0.991300 Mean dependent var12.49618Adjusted R-squared 0.990213 S.D. dependent var1.043029S.E. of regression 0.103188 Akaike info criterion -1.572969Sum squared resid 0.255545 Schwarz criterion -1.382655Log likelihood 26.02157 F-statistic 911.5572Durbin-Watson stat 1.719049 Prob(F-statistic) 0.000000

2. Regress one Independent Variable with all other Independent Variables. Let us see on e view

Adjusted R2 very highIt shows that new dependent variable can be explained by other explanatory variables

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Existance of Multicolliniarity is confirmed.Let us have next trial by removing one of the Independent VariableLet us check the model by removing CIThis can be seen on Eview


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Trail 3: Result Check for AutocorrelationSince this is a time series data, we can see from Durbin Watson statistics, i.e. 0.81 that shows there is positive auto correlation


Both F value and all t values are statistically significant, it appears that there is no multicolliniariry. However this can be confirmed only after removal of Autocorrelation

Let us first address problem of Autocorrelation, by introducing error term

Let us see on E-view

Dependent Variable: CO2EMI_MMTMethod: Least SquaresDate: 04/13/10 Time: 19:32Sample (adjusted): 1 28Included observations: 28 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C -1003.482 62.79131 -15.98122 0.0000EI_TOE_PER_M_INR 156.6583 21.48300 7.292200 0.0000


R-squared 0.997154 Mean dependent var 755.3959

Adjusted R-squared 0.996798 S.D. dependent var 310.8044S.E. of regression 17.58640 Akaike info criterion 8.703693Sum squared resid 7422.756 Schwarz criterion 8.894007Log likelihood -117.8517 F-statistic 2803.013Durbin-Watson stat 0.812457 Prob(F-statistic) 0.000000

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Trail 4: ResultCheck for Autocorrelation

Already removed by introducing error term


Both F value and all t values are statistically significant, it appears that there is no multicolliniariry.

This was ascertained by other tests as well

High adjusted R2 value

Very high f statistics and very High t statistics values indicates this Model is statistically significant.

Dependent Variable: CO2EMI_MMTMethod: Least SquaresDate: 04/13/10 Time: 19:46Sample (adjusted): 2 28Included observations: 27 after adjustmentsConvergence achieved after 11 iterations

VariableCoefficien

t Std. Error t-Statistic Prob.

C -1165.755 125.1889 -9.311962 0.0000EI_TOE_PER_M_INR 183.3898 19.75295 9.284173 0.0000


AR(1) 0.547958 0.152062 3.603506 0.0016

R-squared 0.998386 Mean dependent var 772.5153Adjusted R-squared 0.998093 S.D. dependent var 302.9736S.E. of regression 13.23094 Akaike info criterion 8.168569Sum squared resid 3851.272 Schwarz criterion 8.408539Log likelihood -105.2757 F-statistic 3402.828Durbin-Watson stat 2.228084 Prob(F-statistic) 0.000000

Inverted AR Roots .55

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Final Model definition

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Positive values of all coefficients of Explanatory variables: It shows that there is positive relationship between all

explanatory variables and Co2 emissionIndia's Climate Change Stand:

India never agreed to any proposal for absolute reduction in Co2/GHG emission

Model shows that this can be possible only when either Population start decreasing or country stop moving on the path of development

Both of these are distant possibilities. focus should be given on harnessing energy from clean

sources to curb carbon emissions, which would not affect the country’s economic growth.

Thus India's Stand is correct. World Bank in its report Says “India Right In Resisting

Mandatory Emission Reductions”

Analysis

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This Model shows thatFor every 1 million increase in population, there

will be 0.6977 MMT co2 will be emitted for additional energy use by them.

351 MT Co2 will be emitted from the energy utilized in earning additional 1 Crore of Rupees provided population and Energy intensity remain constant

If we could manage to reduce energy intensity by just 1%, we could able to save 1.83 MMT of energy for same GDP and population. This result into generation of about 1.83 million Carbon Credit; that can be sold in Carbon Market

Analysis

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Only way to reduce Co2 emission is by reducing Energy Intensity.

Data shows that since 1995, without any specific policy, Energy Intensity is decreasing over the years.1995: 219 M TOE per Million $2007: 164 M TOE per Million $

Minister promised the same in Parliament to reduce energy intensity by 25% by 2025 1.e. to bring it about 135 M TOE per Million $

Reducing Energy Intensity will help India financially by selling carbon credit in Carbon Market

Analysis: Policy Initiatives

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Model also help in policy maker to decide carbon tax for those industries which requires more Energy intensity than National Average.

Industries, which are using advance low carbon technology, may get profit by selling carbon saved in Carbon Credit market.

Model may also help policy makers to introduce carbon tax to fund low carbon technology research.

Such Carbon Tax can be calculated by using Model as how much Co2 emitted for unit profit and some percentage of market cost of that ton of Co2

This shows that India has very big opportunity is such severe problem of Climate Change.

Analysis: Policy Initiatives

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Thanks

Documents

Econometric Model of Carbon Dioxide Consumtion